Alex Steer

2571 words | ~13 min

I'm going to start the new year off on a controversial note – not with a prediction (predictions are overrated) but with an observation. I think most of the chatter and hype about data science in marketing is looking in the wrong direction.

This is a bit of a long read, so bail out now or brace yourself.

I've worked in marketing analytics, marketing technology, digital marketing and media for the last decade. I've built DMPs, analytics stacks, BI tools, planning automation systems and predictive modelling tools, and more than my fair share of planning processes. I am, it's fair to say, a marketing data nerd, and have been since back when jumping from strategy to analytics was considered a deeply weird career move.

My discipline has become, slowly-then-quickly, the focus of everyone's attention. The industry buzzwords for the last few years have been big data, analytics, machine learning and AI. We're starting to get to grips with the social and political implications of widespread data collection by large companies. All of this makes data-driven marketing better and more accountable (which it badly needs). But all of this attention - the press coverage, the frenzied hiring, the sales pitching from big tech companies, all of it – has a bias built into it, that means talented data scientists are working on the wrong problems.

The bias is the false assumption that you can do the most valuable data science work in the channels that have the most data. That sounds self-evident, right? But it is, simply, not true. We believe it's true because we confuse the size and granularity of data sets with the value we can derive from analysing them.

Happiness is not just a bigger data set

We're used to the idea that more data equals better data science, and therefore that by focusing on the most data-rich marketing channels, you will get the best results. We are told this every day and it is a simple, attractive story. But the biggest gains in marketing science come from knowing where to be, when to appear and what to say, not how to optimise individual metrics in individual channels. Knowing this can drive not just marginal gains but millions of pounds of additional profit for businesses.

This makes lots of people deeply uncomfortable, because it attacks one of the fundamental false narratives in marketing science: that the road to better marketing science is through richer single-source data. This narrative is beloved of tech companies, but it comes from an engineering culture, not a data science culture. Engineers, rightly, love data integrity. Data scientists are able to find value from data in the absence of integrity, by bringing a knowledge of probability and statistics that lets us make informed connections and predictions between data sets.

Marketing data is the big new thing, but from the chatter, you would believe that the front line of marketing analytics sits within the walled gardens of big data creators like Google, Facebook, Amazon or Uber. These businesses have colossal amounts of user data, detailing users' every interaction with their services in minute detail. There is, to be sure, massive amounts of interesting and useful work to be done on these data sets. These granular, varied and high-quality data resources are a wonderful training ground for imaginative and motivated data scientists, and some of the more interesting problems relate to marketing and advertising. For example, data scientists within the walled gardens can work on marketing problems like:

• How do I make better recommendations based on people's previous product/service usage?
• How do I find meaningful user segments based on behavioural patterns?
• How do I build good tests for user experience features, offers, pricing, promotions, etc?
• How do I allocate resources, inventory, etc., to satisfy as many users as possible?

All of which is analytically interesting and important, not to mention a big data engineering challenge. But if you're a data scientist and particularly interested in marketing, are these the most interesting problems?

I don't think they are.

These are big data problems, but they are still small domain problems. Think about how much time on average people spend in a single data ecosystem (say, Facebook or Amazon), and the diversity of the behaviours they exhibit there. You are analysing a tiny fraction of someone's behaviour; worse, you are trying to build predictive models from the slice of life that you can observe in minute detail. If you work in operations or infrastructure, almost all the data you need sits within the platform. But if you are doing marketing analytics, swimming in the deep but small pool of a single data lake can cause a serious blind spot. How much of someone's decision to buy something rests on the exposure to those marketing experiences that you happen to have tracked through that data set?

As a marketing scientist you have an almost unique opportunity among commercial data scientists: to build the most complete models of people's decision-making in the marketplace. Think about the last thing you bought: now tell me why you bought it. The answer is likely to be a broad combination of factors… and you're still likely to miss out some of the more important ones. As marketing scientists we're asked to answer that question, on a huge scale, every day in ways that influence billions of dollars of marketing investment.

We need bigger models, not just bigger data

Marketing analytics is a data science challenge unlike most others, because it forces you to work across data sets, often of very different types. The machine learning models we build have to be granular enough to allow tactical optimisation over minutes and hours, and robust enough to sustain long-range forecasts over months and years.

The kinds of questions we get to answer include:

• What is the unique contribution of every marketing touchpoint to sales/user growth/etc?
• Can we predict segment membership or stage in the customer journey based on touchpoint usage? How do we predict the next best action across the whole marketing mix?
• How do touchpoints interact with each other or compete?
• Are there critical upper and lower thresholds for different types of marketing investment?
• How sensitive are buyers to changes in price? What other non-price features would get me the same result as a discount if I changed them?
• How important is predisposition towards certain brands or suppliers? What is the cost and impact of changing this vs making tactical optimisations while people are in the market?

Yet we have a massive, pervasive blind spot. We are almost all acting as if marketing science applied only to digital channels. Do a quick Google for terms like 'automated media planning' or 'marketing optimization'. Almost all of the papers and articles you will find are limited to digital channels and programmatic/biddable media. I have had serious, senior people look me in the eye and tell me there is no way to measure the impact of brand predisposition on market share, no way even to account for non-direct-response marketing touchpoints like television, outdoor advertising or event marketing. This is, of course, wrong.

Everywhere you look, there is an unspoken assumption that the whole marketing mix is just too complicated to be analysed and optimised as a whole – that the messy, complex landscape of things people see, from telly ads to websites to shelf wobblers, needs to be simplified and digitised before we can make sense of it. It's little surprise that this idea, that anything not digital is not accountable, is projected loudest by businesses who make their money from digital marketing.

Again, this is an engineering myth, not a data science reality. Engineers, rightly, look at disunited data sets and see an integrity problem that can be fixed. Data scientists should (and I hope do) look at the same data sets and see a probability problem worth solving. The truth is that it is possible to use analytics and machine learning to build models that incorporate every marketing touchpoint, and show their impact on business results. The whole of media and marketing planning is a science and can be done analytically – not just the digital bits. Those who claim otherwise are trying to stop you from buying a car because all they know how to sell you is a bicycle.

This is the part that makes people uncomfortable – because it requires a more sophisticated data science approach. Being smart within a single data set is relatively easy – getting access to the data is a major engineering problem, but the data science problems are only moderately hard. As a data scientist within a single walled garden, it's easy to feel a sense of completeness and advantage, because only you have access to that data. Working across data sets, building models for human behaviour within the whole marketplace, needs a completely different mindset. There is no perfect data set that covers everything from the conversations people have with their friends to the TV programmes they watch to the things they search for online to the products they see in the shops – yet we need to build models that account for all of this.

Probability beats certainty… but it's harder

Making the leap from in-channel optimisation to cross-channel data science means having a better understanding of the fundamentals of probability theory and the underlying patterns in data. For example, I've built models that predict the likelihood that people searching for a brand online have previously heard adverts for the brand on the radio, and the optimum number of times they should have heard it to drive an efficient uplift in conversion to sales. If I had a data set that somehow magically captured individuals' radio consumption, search behaviours and supermarket shopping, this would be a large data engineering problem (because there'd be loads of data) but a trivial data science problem (because I'd just be building an index of purchasing rate for exposed listeners vs a matched control set of unexposed, etc.). This is the kind of analysis that research and scanning panel providers have been doing for decades - it's only the size of the data set that's radically new.

But of course, that data set doesn't exist. It's unlikely it'll ever exist, because the cost of building it would be far in excess of the commercial interests of any business. (Nobody is in the 'radio, online search and grocery shopping' industry… at least not yet. Amazon, I'm looking at you.) So what do we do?

The engineering response is to try and build the data set. This is a noble pursuit, but it can lead to an engineering culture response, which is to try and change human behaviour so that people only do things that can be captured within a single data set. An engineering culture will try to persuade advertisers to shift their spend from radio to digital audio, and their shopping from in-store to online, because then you can track all the behaviours, end to end. So measurement becomes trivial - it's just that, to achieve it, you've had to completely change human behaviour and marketing practice, and build a server farm the size of the moon to capture it all.

The data science response is to look at it probabilistically - to create, for example, agent-based simulations of the whole population based on the very good information we have about the distribution of occurrence of radio listening, online search and supermarket shopping. To do this, you need to be able to master the craft of fitting statistical models without overfitting them - building a model of exposure and response that is elegant, both matching reality but capable of predicting future change and dealing with uncertainty. When you do this, it's possible to build very sophisticated models that give a good guide to how the whole marketing mix influences present and future behaviour, without trying to coerce everything into a single data set.

Data science cultures are vastly better suited to transforming the future of marketing than engineering cultures. They see ambiguity as a challenge rather than an error, and they look hard for the underlying patterns in population-level data. They build models that focus on deriving greater total value from the marketing mix, through simulation and structural analysis across data sets, rather than just deterministic matching of individual-level identifiers. With apologies to Michelle Obama: when they go deep, we go wide.

Data science cultures may not be where you think

Marketing needs to change, and data is going to be fundamental to that change, as everybody has been saying for years. The discipline needs to be treated as a science, and the agencies, consultancies and platforms that want to survive in the next decade need to make a meaningful investment in technology, automation and product.

But while everybody is looking to the engineering cultures of Silicon Valley for salvation, I think the real progress is going to be made by data science cultures - the organisations that combine expertise in statistical data science, data fusion, research and software development, to create meaning and value in the absence of data integrity. Google, to its credit, is one of these. Some of the best original statistical research on the fundamental maths of communications planning is being done in the research group led by Jim Koehler and colleagues.

My employer, GroupM (the media arm of WPP), is another. Over the last few years we've quietly built up the largest single repository of consumer, brand and market data anywhere, of which the big single-source data sets are just one part. We are in the early years of throwing serious data science thinking at that data, building models and simulations for market dynamics that no single data set could hope to capture. Some of the other big media holding companies have strong data science cultures and impressive talent. There are a handful of funded startups, too - but vanishingly few, relative to the tidal wave of investment behind data engineering firms and single-source data platforms.

This is a deeply unfashionable thing to suggest, but a lot of the most advanced marketing science work is being done in media companies and marketing research firms, not in technology companies. There are two reasons for this. First, the media business model has supported a level of original R&D work for most of the last decade, even if it's not always been turned systematically into product. Second, media companies and agencies are ultimately accountable for what to invest, where, how much, and when - the kind of large-scale questions that can't be solved simply by optimising each individual channel to the hilt. (On a personal note, this is why I moved from digital analytics into media four years ago - the data is more diverse, the problems harder and more interesting.)

While everybody is focusing on the data engineering smarts of the Big Four platforms, keep an eye on the data science cultures who are transforming a huge breadth of data into new, sophisticated ways of predicting marketing outcomes. And if you're a data scientist interested in marketing, look for the data science cultures not just the engineering ones. They're harder to find because money and fame aren't yet flowing their way… but they have a big chance of transforming a trillion-dollar industry over the next few years.

804 words | ~4 min

This originally appeared in the 50th edition of Campaign. It's co-written by my colleague David Fletcher.

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Nothing brings us together like a good theoretical disagreement, does it? For an industry built on a reputation for persuasion, we are rather fond of picking sides. This can be on almost any topic, but the prize fight of the year, possibly even the decade, is over the question of data and targeting.

This clash of advertising cultures has become so profound that those on either side no longer seem to be talking the same language. In the red corner are those who argue that the era of mass communication is dead, and that highly targeted, in-the-moment interventions to fragmented 'segments of one' will determine what people buy and why. In the blue corner, we have the defenders of scale, reminding us that broadcast media packs a bigger punch, that brands need to reach new buyers and that costly signalling makes them more desirable in a market driven as much by emotion as logic.

The problem is, we are asking our clients to referee – and that's an exhausting distraction from the 'day job' of solving business problems. Marketers know that the right answer is both: both shared experiences and precision, brand-building and demand fulfilment. They are looking to us – and others – for guidance on how to do both together, and do them well.

Marketing is more than a bit like fishing. Sometimes you fish with a net: there is value in catching lots of potential buyers all at once, even if some aren't ready yet and need to be thrown back for another day. Sometimes, you fish with a spear: you go after individuals because they're easy to spot and disproportionately appealing. And sometimes, you fish with dynamite: you throw something new into the water and blow everything up.

Our agency, Wavemaker, is only 10 months old, born in one of the most disruptive periods our industry has seen for decades. When we put the words "media, content and technology" outside our door, it was out of a sense of shared frustration with the "two tribes" thinking that leads to clients having to act as peacemakers and interpreters between their partners. We're building a large agency of specialists in different client verticals and marketing disciplines, none of whom claim a monopoly on the right answer.

We've now organised those specialists into three large disciplines: Wavemaker Media creates shared experiences for brands (fishing with a net); Wavemaker Content makes ideas and partnerships that shift brand perceptions (fishing with dynamite); and Wavemaker Precision brings all our digital marketing, ecommerce, analytics and technology experts into one team to deliver targeted relevance (fishing with a spear). Our insight, effectiveness, strategy and client-delivery teams operate across all three (fishing where the fish are). We've done this to simplify our offer to clients, and help them accelerate their own transformation by giving easier access to the right expertise, configured in the right way.

Clients' most urgent need is in precision marketing, as the fusion of digital media, search, ecommerce, CRM and tech is now known. Most businesses have digital transformation as a C-suite priority, and this means taking control of their data and technology investments, reorganising the marketing, sales and commerce functions around customer intelligence, and integrating media with digital user experience. Most agencies talk a good game with precision marketing but few deliver it in practice, and this includes many of the specialist performance agencies that make the most noise in this space.

We find there are three factors involved in making a successful leap from performance to precision. First, a focus on growth audiences. Too much digital targeting is based on reaching people who are easy to find or who respond well, rather than those who represent real and distinct sources of growth. This leads to bland demographic targeting instead of intelligent data use; or, worse, false optimisation towards digital hand-raisers.

Second, real data scrutiny. Much digital data makes promises it cannot possibly live up to (can you really target introverted low-fat cheese spread consumers in Leamington Spa?), and off -the-shelf attribution models give a wildly inaccurate view of marketing contributions. Precision means building up trusted data assets and measurement approaches, not just box-ticking.

Third, obsessive deployment. DIY digital buying is easier than ever. Clients need certified activation, tech and analytics experts who will work flexibly and collaboratively to squeeze every last bit of performance out of their tech stack and marketing platforms. Vague claims or mysterious proprietary tools are no substitute.

Clients and agencies who focus on these three things – growth audiences, data integrity and obsessive deployment – can avoid the theoretical debates and focus on finding new ways to do what great advertising has always done: building strong brands, delighting customers and driving growth.

697 words | ~3 min

In marketing, as in so much of life, there are two types of advantage: temporary and sustained. This is obvious when you think about it, but thinking is hard.

Temporary advantage comes from doing the same thing as your competitors, but better, for a while. Most industry depends on temporary advantage. You may temporarily have better robots on your production line, better debt financing, better refund policies on your products, etc. Temporary advantage is driven by tactics, which let you grow share by getting ahead of the competition.

In marketing communications, temporary advantage comes through optimisation: better targeting of your advertising, better scheduling and allocation of your spend, faster or sharper algorithms to bid on placements or search keywords, and so on. Advertising tactics pay back handsomely for fairly early adopters, until most of their competition have the same capabilities. Temporary advantage is rewarding because the gains from it are realised very quickly (when you can suddenly do something others can’t), and decay quite slowly (as others catch up with you at different speeds).

Sustained advantage is more expensive and pays off more slowly, but it is structural. In most sectors, intellectual property is the only source of sustained advantage: patents, trademarks, and strong brands.

The last two of these sound the same but aren’t: a trademark is the protection of your identity, a brand is the identity worth protecting. Brand equity is the value attributable to a brand’s ability to influence purchase in spite of tactics. It is the sustained advantage created by communications and customer experience (both broadly defined).

To take a simple example: at today’s prices, you can buy a 420g tin of baked beans in Tesco for three prices: 32p (Tesco brand), 65p (Branston) or 75p (Heinz). Assuming that the quality of the beans is much of a muchness (ie roughly equal numbers of people would reject each in a blind test), it’s reasonable to say that Branston is carrying about 33p in brand equity, Heinz about 43p. In other words the value of the Heinz brand is worth 34% more to its buyers than the whole tin of Tesco beans. Now that’s a sustained advantage, built over years, paying off over years, but quantifiable.

The obvious question is: is it sustainable? Maybe not. If the Heinz brand didn’t continue to have distinctiveness in people’s minds – that mix of recognisability, emotional reassurance and legitimate beliefs that make people reach for it. or click for it or ask their Amazon Echo for it, despite the price premium – it would lose that pricing power. However, it would do so slowly, over years and not weeks. A strong brand is a battery that is slow to charge, slow to drain.

Much of the drama and debate in marketing at the moment seems to hinge on whether different groups of people are more interested in temporary or sustained advantage. There are obviously vested interests here. Technologists and management consultancies tend to like temporary advantage because they are complex to realise (mainly involving technology and data these days) and they decay fast, ensuring repeat business. Creative agencies tend to like sustained advantage because it requires real insight and creativity to realise (mainly involving very good design and writing) and it requires craft, ensuring repeat business.

The rather obvious answer is that you need both. Temporary advantage generates sudden improvements in revenue or profit, which keeps shareholders and executives happy. Sustained advantage creates ongoing revenue and profit and is an insurance policy against future austerity, allowing you to keep making money even if your product or service is temporarily uncompetitive.

There’s no magic formula for the right balance but there is a guideline: ignore any professional services business that tells you that either temporary or sustained advantage is unimportant. If a consultant or technologist says that mass communication is dead and only hyper-relevant brand experiences matter, they are trying to sell you software. If a creative agency says that tactical communications don’t matter and big ideas are all that count, they are trying to sell you expensive advertising. You may be right to buy both, but don’t ignore either.

770 words | ~4 min

Years ago, when they said that social media would kill advertising, I imagined they were talking about the decline of the full-page print ad or the thirty-second spot. Now I realise what they meant. Open Twitter during Cannes week and you will see half the industry there, gleefully beating itself to death in full view of its clients.

From the tenor of the conversation, you would honestly believe that all advertising were a doomed enterprise. We are no longer creative, we do not have the ear of our clients, and the public do not care for us. The nation's tellies go unwatched, its cinemas are empty, its billboards tatter at the edges. What's physical is irrelevant, what's digital is fraudulent, and our influencers aren't influencing. We are blocked, bagged, ripped off, subscribed around, non-viewable, unfit for platform. Our impressions are not impressive.

Imagine being a marketer and reading this. Better yet, imagine being one of the professional services or technology companies treading the fringes of our trade, who do not seem to share our lack of confidence in the commercial value of what we do.

Data, apparently, is to blame. Data - the mere having of it - drives out original thinking, latching itself to businesses like carbon monoxide, preventing the fragile oxygen of creativity from having a chance. My fellow number-curmudgeons and I have ruined everything with our spreadsheets and our models and our research and our maths. Our dreadful machines have forced out all that is good and replaced it with (always, in this diatribe) a pair of shoes that follow you round the internet. We are fools for letting it happen to us, and our clients are fools for buying it.

In the words of that Sony ad: balls.

Sorry, but time's up. On blaming data for lack of bravery, on pummelling our industry in public, on treating our clients like fools for choosing us, and on the 'two tribes' mentality that treats our creative and our analytical people as opponents rather than collaborators.

No industry in the world evolves and adapts like ours. There are strains of the ebola virus with less agility and will to survive. The things we tell our clients to do every day - think round corners, organise around people, move fast with a sense of direction - are the things we do ourselves. In doing so, we create disproportionate, unfair, unreasonable gains for our clients, vastly in excess of the fire-sale value of their corporate assets. Only communications improves the value of a company merely by adjusting the perceptions of its would-be buyers. The financial value of the world's top hundred brands has more than doubled in the last decade while we sit here wondering if what we do makes any difference.

So don't tell me that data is ruining it. Analytics - the intelligent use of data - is the fastest route past the ordinary that I know. If all data told you was how to be safe and how to stay the same, there'd be no call for it. Looking deeply, clearly and thoughtfully at the numbers generated by a business, its audiences and its advertising lets us spot the un-obvious things that will lead to growth. What better way to find the space for creativity to transform our experience of a brand, whether shared or personal? What better stimulus to make something genuinely new?

But to do that, we need to be proud of our data side, and we need to hire and retain people who bring that analytical talent - human curiosity with statistical integrity - to work with them every day. To hire people who use data as a pogo stick, not a crutch - and who encourage their clients to do the same.

Our clients are as brave as we empower them to be - braver, often, since they have to stare down their sceptics across the boardroom table and defend our ideas. If we pick holes in the safety net, are we that surprised when they don't jump?

It's time to stop treating ignorance of analytics as a virtue. It doesn't make you more creative, it just makes it more likely that you're pointing your brilliance in the wrong direction. We have a vast amount of knowledge about how communications drives growth - more than our clients, more than our competitors. Let's teach our creatives to stand up for the value of evidence, our analysts and technologists the value of ideas. And let's show our clients that an agency - by definition a collection of do-ers - is a thing worth being proud of.

825 words | ~4 min

Right then, marketing industry, we need to talk. See, there’s this story going round that the future of brands is under threat from algorithms. It’s nonsense, and it does disservice to our trade.

Most versions of the story go like this. Over the last decade or so, and especially in the last few years, media consumption has switched dramatically from environments controlled by editors, to environments controlled by algorithms, which filter and prioritise the content we see (hear, watch, etc.) based on knowledge of our own preferences, generated through machine learning. I talked about this to the Advertising Association in early 2017, and I raised the prospect that as the use of algorithmic decision-making extends from media prioritisation to e-commerce, existing brands might have to work a bit harder to make sure that they don’t get relegated to becoming back-end service providers. For example, if I am constantly asking the Amazon Echo on my kitchen counter to tell, sell or play me stuff, I may lose the sense of regularly interacting with the brands who supply those services (Spotify, National Rail, Jamie Oliver, etc).

But the narrative of ‘algorithms vs brands’ is taking this to a ludicrous extreme. Take for example this extraordinary rundown from Scott Galloway:

Brands are shorthand for a set of associations that consumers use for guidance toward the right product. CPG brands have spent billions and decades building brand via messaging, packaging, placement (eye level), price, creative, endcaps, etc. The internet loses much of this, since the impact of zeroes and ones is no match for atoms, and much of the design and feel of the product loses dimension, specifically from three to two (dimensions). As a result, the internet has become a channel to harvest, rather than build, brands.

However, all these weapons of brand … all of them go away with voice. No packaging, no logos, no price even. The foreshadowing of the death of brand, at the hand of voice, can be seen in search queries.

Crikey.

Before we go any further, for some reason ‘brand’ is one of those terms that everybody seems to interpret differently. Which is surprising, because a company’s brand is normally its single most valuable asset, typically account for about two-thirds to three-quarters of volume across a year. You would think that, as an industry, we’d understand this and be pretty clear about what a brand is, the way that businesses tend to be pretty clear about what a pension fund or a manufacturing plant is. So, for the avoidance of doubt, I define a brand as a recognisable identity of a business in the marketplace, which creates value by increasing demand and discovery for its products or services.

So, I think this ‘death of brands’ narrative is rubbish. Not just because it’s not true, but because it’s the opposite of the truth. Let’s be loud and clear on this one.

The environment the scare stories describe, is the exact environment that brands were built for.

Cast your mind back to the mid nineteenth century in Western Europe and the young United States. As the economy went through a series of dramatic structural shifts, large populations began to urbanise and living standards went up, and with them so did competition for goods and services. Manufacturers began to find their profits under threat by new intermediaries. These intermediaries were large, powerful, and had enormous and rapidly growing user bases (as we’d now call them). Their power was cited as unfair influence, as the death of the manufacturer, as a slippery slope towards commoditisation and a race to the bottom. These intermediaries exercised almost total control over the goods that people saw, they could put substantial pressure on pricing, and their customers loved them for it.

They were called shops.

So manufacturers began to invest in raising the profile of their products in people’s minds. They used media to push back against the price wars and the margin pressure. They used creativity to make their products more appealing, more pleasing, more meaningful and differentiated — so that customers would ask for them by name, and so that shops would look bad if they did not stock them.

Sounding familiar yet?

Brands thrive under this sort of pressure, because they become the only unfair advantage that a business can deploy. Algorithms and ecommerce won’t kill brands, they will kill some brands, and they will raise the stakes. If your route to market involves someone standing in a kitchen and asking a plastic and metal box to ship a product to you, you need to make sure you’re already in the kitchen, by being in the mind. And there is a very good, reliable, extremely well proven mechanism for making that happen.

It’s called brand advertising. Ask for it by name.

2169 words | ~11 min

Right then. It's been almost a year since I last posted here - a year in the life of my agency, Maxus, that I look forward to talking about in more detail in future when the paint has dried. (Short version: IPA Effectiveness awards; I became CSO; restructure and retraining; building new cross-media planning tech; best agency in Top 100 Companies to Work for list; big new business win; merger with MEC to become Wavemaker as of Jan 2018.)

For now, a few notes on an idea that sits behind an increasing amount of what we do, and talk about, as an industry: optimisation.

First, a quick definition. Optimisation is the use of analytical methods to select the best from a series of specified options, to deliver a specified outcome. These days, a lot of optimisation is the automated application of analytical learning. I wrote a long piece last year on some of the basic machine learning applications: anomaly detection, conditional probability and inference. Optimisation can take any of these types of analysis as inputs, and an optimiser is any algorithm that makes choices based on the probability of success from the analysis it has done. Optimisation crops up in all sorts of marketing applications, that we tend to discuss as if they were separate things:

• Programmatic buying
• Onsite personalisation
• Email marketing automation
• AB and multivariate testing
• Digital attribution
• Marketing mix modelling
• Propensity modelling
• Predictive analytics
• Dynamic creative
• Chatbots

...and so on, until we've got enough buzzwords to fill a conference. All of these are versions of optimisation, differently packaged.

When I say optimisation 'sits behind' a lot of what we do in marketing and media today, it's because optimisation is almost the opposite of an industry buzzword: a term that has remained surprisingly constant in marketing discourse over the last few years, while its application has broadened considerably. By way of lightly-researched reference, here are Google search volume trends for 'optimisation' and 'machine learning' in the UK over the last five years (it makes little difference, by the way, if you search for the US or UK spelling):

Search volumes for optimisation (blue) have remained fairly constant over the last half-decade (and are driven mainly by 'search engine optimisation'), whereas 'machine learning' (red) has risen, and crossed over in early 2016. I show this as just one cherry-picked example of a tendency for marketing language to imply that there is more innovation in the market that actually exists. We can see this more clearly by looking at the phenomenon of hype clustering around machine learning.

Hype clustering

Let's look back at the Gartner Hype Cycle, the canonical field guide to febrile technology jargon, from July 2011:

We can see a good distribution of technologies that rely on optimisation, all the way across the cycle: from video analytics and natural-language question answering at the wild end, to predictive analytics and speech recognition approaching maturity.

Fast forward six years to the most recent hype cycle from July 2017:

'Machine learning' and 'deep learning' have found their way to the top of the hype curve... while everything else on the list has disappeared (except the very-far-off category of 'artificial general intelligence'). Fascinatingly, machine learning is predicted to reach maturity within 2-5 years, whereas some of the technologies previously on the list six years ago were predicted to have matured by now. In other words, several of the technologies that were supposedly past the point of peak hype in 2011 are now back there, but rechristened under the umbrella of machine learning.

Machine learning is a classic piece of hype clustering: it combines a lot of analytics and technical methods that are themselves no longer hypeworthy, with a few that are still extremely niche. The result is something that sounds new enough to be exciting, wide-ranging enough to be sellable to big businesses in large quantities - very much the situation that big data was in when it crested the hype cycle in 2013.

Sitting behind a lot of 'machine learning' is good old-fashioned optimisation, albeit increasingly powered by faster computing and the ability to run over larger volumes of data than a few years ago. Across digital media, paid search, paid social, CRM, digital content management and ecommerce, businesses are beginning to rely hugely on optimisation algorithms of one sort or another, often without a clear sense of how that optimisation is working.

This is, it won't surprise you to learn, hugely problematic.

Doing the right thing

Optimisation is the application of analysis to answer the question: how do I do the right thing? Automated mathematical optimisation is a very elegant solution, especially given the processing firepower we can throw at it these days. But it comes with a great big caveat.

You have to know what the right thing is.

In the disciplines where automated optimisation first sprung up, this was relatively straightforward. In paid search advertising, for example, you want to match ad copy to keywords in a way that gets as many people who have searched for 'discount legal services' or 'terrifyingly lifelike clown statues' to click on your ads as possible. In ecommerce optimisation, you want to test versions of your checkout page flow in order to maximise the proportion of people who make it right through to payment. In a political email campaign, you want as many of the people on your mailing list to open the message, click the link and donate to your candidate as possible. In all of these, there's a clear right answer, because you have:

1. a fixed group of people
2. a fixed objective
3. an unambiguous set of success measures

Those are the kinds of problems that optimisation can help you solve more quickly and efficiently than by trial and error, or manual number-crunching

The difficulty arises when we extend the logic of optimisation, without extending the constraints. In other words, when we have an industry that is in love with the rhetoric of analytics and machine learning, that will try and extend that rhetoric to places where it doesn't fit so neatly.

False optimisation

Over the last few years we've seen a rush of brand marketing budgets into digital media. This is sensible in one respect as it reflects shifting media consumption habits and the need for brands, at a basic level, to put themselves where their audiences are looking. On the other hand, it's exposed some of the bad habits of a digital media ecosystem previously funded largely by performance marketing budgets, and some of the bigger advertisers have acknowledged their initial naivety in managing digital media effectively. Cue a situation where lots of brand marketers are concerned about the variability of the quality of their advertising delivery, especially the impact of digital's 'unholy trinity' of brand safety, viewability and fraud.

And what do 'worry about variability' plus 'digital marketing' equal? That's right: optimisation.

Flash forward and we find ourselves in a marketplace where the logic of optimisation is being sold heavily to brand marketers. I've lost count of the number of solutions that claim to be able to optimise the targeting of brand marketing campaigns in real time. The lingo varies for each sales pitch, but there are two persistent themes that come out:

1. Optimising your brand campaign targeting based on quality.
2. Optimising your brand campaign targeting based on brand impact.

Both of these, at first look, sound unproblematic, beneficial, and a smart thing to do as a responsible marketer who wants to have a good relationship with senior management and finance. Who could argue with the idea of higher-quality, more impactful brand campaigns?

The first of them is valid. It is possible to score media impressions based on their likely viewability, contextual brand safety, and delivery to real human beings in your target audience. While the ability to do this in practice varies, there is nothing wrong with this as an aim. It can be a distraction if it becomes the objective on which media delivery is measured, rather than a hygiene factor; but this is just a case of not letting the tail wag the dog.

The second looks the same, but it isn't, and it can be fatal to the effectiveness of brand advertising. Here's why.

Brand advertising, if properly planned, isn't designed towards a short-term conversion objective (e.g. a sale). Rather, it is the advertising you do to build brand equity, that then pays off when people are in the market for your category, by improving their propensity to choose you, or reducing your cost of generating short-term sales. In other words, brand advertising softens us up.

Why does this matter? Because optimisation was designed to operate at the sharp end of the purchase funnel (so to speak) - to find the option among a set that is most likely to lead to a positive outcome. When you apply this logic to brand advertising, these are the steps that an optimiser goes through:

1. Measure the change in brand perception that results from exposure to advertising (e.g. through research)
2. Find the types of people that exhibit the greatest improvement in brand perception
3. Prioritise showing the advertising to those types of people

Now, remember what we said earlier about the three golden rules of optimisation:

1. a fixed group of people
2. a fixed objective
3. an unambiguous set of success measures

Optimising the targeting of your brand advertising to improve its success metrics violates the first rule.

This is what we call preaching to the nearly-converted: serving brand advertising to people who can easily be nudged into having a higher opinion of your brand.

It is false optimisation because it confuses objectives with metrics. The objective of brand advertising is to change people's minds, or confirm their suspicions, about brands. A measure for this is the aggregate change in strength of perception among the buying audience. DIagnostically, research can be used to understand if the advertising has any weak spots (e.g. it creates little change among older women or younger men). But a diagnosis is not necessarily grounds for optimisation. If you only serve your ads to people whose minds are most easily changed, you will drive splendid short-term results but you will ultimately run out of willing buyers, by having deliberately neglected to keep advertising to your tougher prospects. It's the media equivalent of being a head of state and only listening to the advice of people who tell you you're doing brilliantly - the short-term kick is tremendous, but the potential for unpleasant surprise is significant.

Preaching to the valuable

The heretical-sounding conclusion is: you should not optimise the targeting of your brand campaigns.

Take a deep breath, have a sit down. But I mean it. You can optimise the delivery, by which I mean:

• Place ads in contexts that beneficially affect brand perceptions
• Show your ads only to people in your target buying audience (not to people who can't buy you, or to bots)
• Show better-quality impressions (more viewable, in brand-safe contexts)
• Show creative that gets a bigger response from your target audience

But do not narrow your targeting based on the subsets of your audience whose perceptions of you respond best. That is a fast track to eliminating the ability of your brand to recruit new buyers over time and will create a cycle of false optimisation where you not only preach to the converted, but you only say the things they most like to hear.

Brand advertising is the art of preaching to the valuable. It means finding out which people you need to buy your brand in order to make enough money, and refining your messaging to improve the likelihood that they will. Knowing that requires a serious investment in knowledge and analysis before you start, to find your most viable sources of growth and the best places and ways to advertise based on historic information. This is anathema to people who sell ad-tech for a living, for whom 'test and learn' is of almost theological importance, not least because it encourages more time using and tweaking the technology. The 'advertise first, ask questions later' approach looks like rigour in the heat of the moment (real-time data! ongoing optimisation!) but is the exact opposite.

Testing and learning is exactly the right approach when you have multiple options to get the same outcome from the same group of people. It is precisely the wrong thing to do if it leads to you changing which people to speak to. It's like asking out the girl/boy of your dreams, getting turned down, then asking out someone else using the same line, and thinking you've succeeded. Change the context, change the line, but don't change the audience.

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Last week I had the privilege of attending the IPA Effectiveness Awards, where we picked up a Silver for our work with Plusnet over the past five years. Our paper was called out in the judges' review for creating a culture of sustained effectiveness with an ongoing commitment to rigorous testing. The result is that for every pound spent on advertising (creative, media and agency fees), Plusnet have got over £4 back in new customer revenue.

For me it's a great example of effectiveness done right, by a team of true marketing scientists working alongside a great media planning team, a strong creative agency and a supportive client.

But it also provides a strong contrast to the way I see analytics done in a lot of marketing organisations these days.

A few years back, Harvard Business Review proclaimed data scientist to be the sexiest job of the decade. A quick job search reveals thousands of vacancies, many of them with wildly varying job specs. In fact, right now one of those vacancies is in my own team. By our definition, a data scientist is a statistical developer - someone who can turn data analysis methods into working, scalable code so they can be automated and run faster. This is a vital skill for any team that wants to be taken seriously in a world where the volume of available data to be processed, and the frequency of having to make sense of it, have both increased vastly.

But the rise of the data science star in marketing departments and agencies is not an unqualified good. It mirrors the emergence of a generation of mid-level to senior marketers many of whose training is almost entirely within either digital or direct response businesses. This is due in large part, I suspect, to the reductions in brand marketing spend and headcount in the years after the 2008 financial crash. There is a 'lost generation' of brand marketers and that cohort is now becoming relatively senior but without having had the training in many of brand marketing's craft skills, like designing a brand tracker, interpreting qual research or using econometric modelling.

The result is an assumption in many businesses that marketing analytics is largely just a data and software problem - a view often promoted by technology companies too, unsurprisingly. The result is that we as an industry have been hiring people who can do stats and write code hand-over-fist, calling them data scientists and assuming that they can figure out the complexities of predicting marketing performance.

It's an enormously dangerous tendency, the same one that the financial trading industry fell victim to a decade ago. The reason why is hard to explain if you don't do analytics for a living, but in a nutshell: pure data scientists, without marketing experience, tend to make bad assumptions about how marketing works. They bake these assumptions into their code and their models, and you end up with a badly skewed view of how your marketing works. It's very fast, the code is efficient, and it's fully automated, but it's wrong, like a driverless car accelerating smoothly towards a cliff edge. Worse, if you don't have the ability to check the assumptions (because you can't code, or don't understand the statistics), you have no way of knowing whether the assumptions in your ultrafast machine learning algorithms are brilliant or bogus.

Even when done manually rather than automated, bad assumptions can kill a brand. Often these assumptions are incredibly basic - for instance, I've seen a lot of marketing attribution models that assume that marketing only influences sales on the day (or even in the hour) that people see it, or that a marketing channel only works if it generates a direct response. This is the logic of early 2000s email marketing, being applied to large integrated brand marketing budgets with predictably hilarious but terrible results.

Data science - turning maths into code - is a vital skill, but it is not the whole game. It's time to start valuing marketing science - advanced analytical ability informed by practical experience of working with brands and advertising. It's time to start growing our own talent rather than hiring from outside; time to start training people in the harder disciplines of econometric modelling, research design and segmentation; time to recruit social scientists and economists as well as pure mathematicians and computer scientists; and time to be proud of the bits of what we do that aren't automated as well as the bits that are. Time, in other words, to insist that analytics knows a bit more about the complexities of how real people respond to brands.

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You might have seen in today's news that the trading value of the pound fell by 6% overnight, apparently triggered by automated currency trading algorithms. Here's how that looked on a chart of sterling's value against the dollar:

(Source: FT.com)

It's a fascinating read - we live in a world where decisions made by computers without any human intervention can have this sort of impact. And since 'machine learning' of this kind is a hot topic in marketing right now, and powers a lot of programmatic buying, today's news is a good excuse to think about the basics of how machines learn.

So, here's a a quick guide to how machine learning works, and why the pound in your pocket is worth a bit less than it was when you went to bed (thanks, algorithms).

Anomaly detection: expecting the unexpected

Machine learning is a branch of computer science and statistics, that looks for patterns in data and makes decisions based on what it finds. In financial trading, and in media buying, we need to find abnormalities quickly: a stock that is about to jump in price, a level of sharing around a social post that means it is going viral, or a level of traffic to your ecommerce portal that means you need to start adding more computing power to stop it crashing.

For example, these are Google searches for Volkswagen in the UK over the past five years. See if you can spot when the emission scandal happened.

If you wanted to monitor this automatically, you'd use an anomaly detection algorithm. If you've ever used a TV attribution tool, you've seen anomaly detection at work, picking up the jumps in site traffic that are attributable to TV spots.

Anomaly detectors are used to set triggers - rules that say things like If the value of a currency falls by X amount in Y minutes, we think this is abnormal, so start selling it. This is what seems to have happened overnight to the pound. One over-sensitive algorithm starts selling sterling, which drives down its value further, so other slightly less sensitive algorithms notice and start selling, which drives down the price further, and so on...

Conditional probability: playing the odds

Most decision-making, especially at speed, isn't based on complete certainty. Algorithms need to be able to make decisions based on a level of confidence rather than total knowledge.

For example, is this a duck or a rabbit?

At this point you might say 'I don't know' - i.e. you assign 50:50 odds.

How about now?

You take in new information to make a decision - if it quacks out of that end, it's a duck. (Probably.)

This is conditional probability - updating your confidence based on a new information in context. 'This is either a rabbit or a duck', becomes 'this is a duck, given that it quacks'. We use conditional probability in digital attribution ('what is the likelihood of converting if you have seen this ad, vs if you haven't?') and we use it in audience targeting for programmatic buying: given that I've seen you on a whole load of wedding planning sites, what is the likelihood that you're planning a wedding?

Again, conditional probability can go wrong if we're too strict or not strict enough with our conditions. If I decide you're planning a wedding because I've seen you on one vaguely wedding-related site, I'm probably going to be wrong a lot of the time (known as a high false positive match rate). If I insist that I have to see you on 100 wedding planning sites before I target you as a wedding planner, I'm going to miss lots of people who really are planning weddings (a low true positive match rate).

Currency trading algorithms use conditional probability: given that the value of the pound is down X% in Y minutes, how likely is it that the pound is going to fall even lower? An over-sensitive algorithm, with too high a false positive rate, can start selling the pound when there's nothing to worry about.

Inference: how machines learn, and how we use brands

Anomaly detection and conditional probability are used together to help machines learn and classify, known as inference because computers are inferring information from data.

For example, a few years ago Google trained an algorithm to recognise whether YouTube videos contained cats. It did this by analysing the frame-by-frame image data from thousands of videos that did contain cats.

But it also trained the algorithm on lots of videos that weren't cats. That's because the algorithm is a classifier, designed to assign items to different categories. The Google classifier was designed to answer the question: does this image data look more like the videos of cats I've seen, or more like the videos of not-cats?

Good inference requires these training sets of data so that. A badly-trained classifier will assign too many things to the category it knows best, assuming that everything with a big face and whiskers is a cat.

We use classifiers in audience targeting and programmatic buying, to assign online users to target audience groups. For example, in Turbine (the Xaxis data platform) each user profile might have thousands of different data points attached to it. A classifier will look at all of these and, based on what it's seen before, make a decision about whether a user is old or young, rich or poor, male or female... So inference and classification are vital for turning all those data points into audiences that we can select and target.

But we are also classifiers ourselves - our brains are lazy and designed to make decisions at speed. So when we go into the supermarket we look for cues that the thing we're picking up is our normal trusted brand of butter, bathroom cleaner or washing-up liquid. Retailers like Aldi hijack our inbuilt classification mechanisms to prompt us to choose their own brands:

From metrics to models

There's so much data available to us now - as marketers, as well as stock traders - that we can't look at each data point individually before making decisions. We have to do get used to using techniques like anomaly detection, conditional probability and classification to guide us and show us what is probably the right thing to do, to optimise our media or our creative. Machine learning can help us do this faster and using larger volumes of data. At Maxus we call this moving from metrics to models and it's one of the things we can help clients do to be more effective in their marketing. As we've seen today on the currency market, though, it can be scary and it can have unexpected consequences if not done properly.

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Originally posted on the Maxus blog

Facebook has revealed that for the last two years it has been overstating video completion rates, due to an error in the way it calculates views.

Because Facebook only counts as a 'view' any video consumption over three seconds, it has been applying the same logic to its video completion rate metric - so the metric tells us not how many people who started watching a video then finished it, as we would expect, but how many got past the first three seconds and then finished. It is estimate that their video conversion rates have been overstated by 60 to 80% for the last two years.

Facebook are now hurrying to amend the metric, which they are treating as a replacement, but which is in reality a bug fix.

The news is understandably shocking to advertisers and their agencies, many of whom have been investing heavily in video and using these metrics to monitor and justify spend.

But it is also sadly predictable - an inevitable consequence of the lack of auditability in the metrics produced by many media platforms, not just Facebook.

Facebook have not allowed independent tracking of video completion rates on their platform, meaning that the only way to get video completion data is from Facebook itself. They are not unique in this, and we see this 'metric monopoly' behaviour from many of the digital media platforms, usually citing reasons such as user experience or privacy. Rather than allow advertisers to conduct their own measurement, many platforms are now offering to provide advanced analytics to brands who buy with them, including digital attribution and cross-device tracking. The data and the algorithms that power this measurement remain firmly in the media owner's black box.

Today's news makes it clear how unacceptable an arrangement this is. At Maxus we talk about the importance of 'Open Video' - planning video investment across many channels and touchpoints, reflecting people's changing use of media and making the most of the vast and proliferating range of video types that exist today, from long-form how-tos and product demos to seconds-long bitesize experiences in the newsfeed. As video changes, it creates more opportunities for brands, far beyond the thirty-second spot.

But Open Video requires a commitment to open measurement. As advertisers and agencies we have to be able to gather a coherent, consistent picture of what people are seeing and how content is performing. We are investing significant effort in building the right measurement and technology stack to help clients plan, deliver, measure and optimise Open Video strategies, including advanced quality scoring, attribution and modelling that lets us see how exposure in one channel compares to another in terms of quality, completeness and effectiveness.

Media platforms create amazing new possibilities and are important partners to advertisers and agencies in innovation and delivery. But they should not be allowed to mark their own homework. Measurement and attribution should always be independent of media delivery, available to agencies and auditable by clients. Any other arrangement is a compromise - and, as we've seen this week, a risk.

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Tom Dunn and I wrote this on Brand Republic this week. Reposting...

It’s been an extraordinary couple of weeks on planet video. The TV industry body, Thinkbox, and Google’s YouTube have been engaged in a full and frank exchange of views that, both are at pains to point out, is absolutely not a fight. The topic they are definitely-not-arguing about is a fundamental one: where advertisers should spend their video advertising budgets.

The totally-not-trouble began brewing back in October, with a punchy statement from Google’s UK & Ireland Managing Director, Eileen Naughton, making the case the advertisers should shift 24% of their TV budgets into YouTube, especially if they’re targeting 16-34 year olds.

Last week, Thinkbox came back swinging, calling the Google claim ‘ill-founded and irresponsible’. In the intervening months they have been analysing viewing and advertising data, to find that while YouTube made up 10.3% of 16-24 year-old’s video consumption (v.s TV’s 43.5%), it made up just 1.4% of their video advertising consumption (with TV coming in at a whopping 77.5%).

Within a few days, Google wheeled out their econometric big guns and shot back with an even bigger claim: making the case for advertiser that YouTube offers a 50% better return on investment than that of television, and that 5-25% of video budgets should be spent on YouTube.

Now, it’s definitely not a scrap, but it seems that marketers and agencies are stuck in the middle and in a Brexit kind of way, need to make up their minds where they stand. And worst of all, the kinds of spats that used to be conducted via general pronouncements about consumer trends and attitudes are now being tooled up with findings from data.

Or, should we say, “findings”. From “data”.

Thinkbox and YouTube have stood out in the industry over the years for their commitment to research and measurement. Yet, in the battle of the boxes it seems both have lost focus and the numbers used raise more questions than answers.

As the heads of effectiveness and futures at a media agency, we both spend a lot of our time trying to find the balance between what’s working today and what’s changing tomorrow. This conversation about the impact of video channels matters. Because of the scale of the change we are already seeing in media consumption, and the greater scale of changes to come. Is the leapfrogging of linear TV by online video channels among the under-25s a temporary behaviour or a deeper generational shift? Will the box in the living room lose its next generation of viewers permanently, or will it welcome them back with open arms as a large generation, now house-sharing (or overstaying their welcome with their parents) find themselves with living rooms (and remote controls) of their own.

Either way, the world in which video advertising lives is changing. This stuff matters to all of us who use video to tell stories, make connections and grow our brands. That’s why it’s good to see media owners and industry bodies taking it seriously – but also why the use of data as weaponry has left something to be desired.

In the blue corner, ThinkBox. We’re puzzled by their argument more than by their numbers. They seem to be saying the because more advertising is consumed on TV, clients should advertise on TV more. Yet this comes across as circular logic – saying we should put our ads on TV because that’s where the ads are. If there is a 4:1 ratio of content consumption between TV and YouTube, but a 98:1 ratio of advertising consumption, surely that implies that YouTube has a lot more headroom? It’s fair to say that as consumers we still accept a far higher payload of advertising per piece of content on TV than we do on YouTube, but that’s as much to do with the vastly different buying models, available formats, and modes of consumption than ability of the platforms to deliver exposure.

In the red corner, YouTube, with is headline-grabbing claim of 50% higher ROI. The rationale for this is a study done with Data2Decisions, an econometrics and analytics consultancy. This is a good sign that there will be some robust measurement underpinning this, but more transparency is needed before this can be taken seriously.

The analysis uses a combination of market mix modelling (econometrics) to show the total contribution of TV vs. online video, and ecosystem modelling to dig down into the performance of different individual video channels. This is interesting stuff, and makes for good headlines, but it raises a lot of questions. We think there are three reasons to be cautious.

First, we don’t know what the period of research was, or how many brands, campaigns and categories were included. We don’t know what kind of campaigns they were. Brand-building vs. short-term sales-driving, for example. Like a clinical trial, we need to be confident that if we give you the same budgetary medicine, we know what the side effects might be.

Second, we’ve only seen the headline figures (mainly about ROI). This would be a misguided basis to start shifting huge chunks of budget around.

For example, if we spend £1 million on TV and drive £1.2 million in sales, we have an ROI of £1.20. If we spend £10,000 on YouTube and drive £18,000 of sales, we have an ROI of £1.80. This is 50% higher than TV, but is also delivering far less money. The research headlines don’t tell you what would happen to the ROI if you put more money into YouTube. Would it stay at 50% better than TV or would it start to diminish?

Third, the headlines are only comparing TV and YouTube. To do this properly, we need to understand the relative impact of other video channels to. YouTube’s ROI might be higher than TV’s, but how does it compare to the rest of the online pack?

We welcome the industry taking cross-platform video measurement seriously. At Maxus we have an ‘Open Video’ philosophy to setting video investment strategy, and we are developing tools and technology to plan, measure and optimise across different video channels efficiently and effecitvely. We use market mix modelling and attribution to identify the impact of different video channels, and advanced tracking to make sure that we have a common approach to measuring things like viewability, brand safety and inventory quality across video channels.

That’s why we’re asking both YouTube and ThinkBox to put down their sharpened spreadsheets and to back up the headlines with evidence. It’s not a matter of suddenly shifting money from TV into YouTube, but of understanding what the right channel mix is for individual brands based on their needs, their priorities and their audiences.

Entertaining as the ringside seat has been, advertisers deserve a bit better. It’s time for a grown-up conversation about what’s working now, and what’s changing next.